Predictive Analytics for Database Marketing

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Jarlath Quinn – Analytics Consultant

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FAQ’s

• Is this session being recorded? Yes

• Can I get a copy of the slides? Yes, we’ll email a PDF copy to

you after the session has ended.

• Can we arrange a re-run for colleagues? Yes, just ask us.

• How can I ask questions? All lines are muted so please use the

chat facility – if we run out of time we will follow up with you.

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• Premium, accredited partner to IBM specialising in the SPSS Advanced

Analytics suite.

• Team each has 15 to 20 years of experience working in the predictive

analytic space - specifically as senior members of the heritage SPSS team

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What do we mean by ‘Predictive Analytics’?

Predictive analytics encompasses a variety of techniques from

statistics

and

data mining

that analyze current and historical

data to make predictions about future events

Analysis of structured and unstructured information with mining,

predictive modeling, and 'what-if' scenario analysis.

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What do we mean by ‘Predictive Analytics’?

• It’s different from Business Intelligence or MI reporting

• Actually, it’s not always about prediction

• However, Predictive Analytics does creates important new data

• These data take the form of estimates, probabilities, forecasts,

recommendations, propensity scores, classifications or likelihood values

• Which in turn can be incorporated into key operational and/or insight

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Core Applications in Marketing

Acquire customers:

• Understand who your best customers are

• Connect with them in the right ways

• Take the best action maximize what you sell to them

Grow customers:

• Understand the best mix of things needed by your customers and channels

• Maximize the revenue received from your customers and channels

• Take the best action every time to interact

Retain customers:

• Understand what makes your customers leave and what makes them stay

• Keep your best customers happy

• Take action to prevent them from leaving

grow

attract

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Why is this important to organizations?

• Acquiring customers is expensive

– Not unusual to cost 6 times as much as retaining them

– Understanding who is most likely to convert is very cost effective

• 80% of a company’s profits come from 20% of its customers

– Need to understand these customers needs

– How they behave and what keeps them happy

• Increasing customer retention rates by 5% increases profits by 25% to 95%.

– Study by Bain & Company, working with Earl Sasser of Harvard Business School

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Typical Analytical Applications

• Predictive Modelling

– Campaign Response

– Cross-Sell/Up-Sell

– Customer Acquisition

– Retention Scoring

– Satisfaction Modelling

– Real Time

Recommendations

• Segmentation

– Customer

Behaviour

– Life Time Value

– Loyalty

• Other Applications

– Basket Analysis

– Sequence Modelling

– Sentiment Analysis

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At the heart of Predictive Analytics is the model

• Predictive Analytics uses historical data from many people/incidents

• Age, Gender, Average Spend, Product Category, Region, Tenure etc.

• With known outcomes/results

• Responded, upgraded, defaulted, recommended, cancelled, donated, failed, renewed etc.

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At the heart of Predictive Analytics is the model

• The resultant model is a pattern or formula that can be examined and tested

• Moreover, it can be treated as a

physical object

• Or an important asset that can be

deployed in a wide variety of ways

before being archived

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At the heart of Predictive Analytics is the model

• We can take new data from individuals or incidents…

• Age, gender, average spend, sentiment, tenure, time since last visit

• Using a model based on the same information…

• Generate probability values, likelihood scores and estimates

• In other words…..predictions

32% chance of

cancellation

0.13 probability

of defaulting

Estimated

NPS = 6

Predicted Lifetime

Value = £938

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At the heart of Predictive Analytics is the model

• We can then deploy the predictions through multiple channels to

make better decisions

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• Proactive (outbound)

– Integrated with existing campaigns

– Can set the decision agenda

– Can be planned in advance

– Less costly and simpler than dynamic but also less timely & accurate

• Dynamic (inbound)

– Offers based on new data in real time

– Opportunity to gather new, important information

– Opportunity to revise offer/action – e.g. retain, cross-sell

– Outcome can be captured immediately

– Requires greater investment than proactive approaches

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In Marketing, Predictive Analytics enhances the existing

campaign selection process..

Deceased

Tenure < 3

Months

Do Not

Mail

No Longer

Customers

No

Discount

Campaign

Selection

Rules

Global Suppression

Rules

Likelihood to Respond

?

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By finding the most likely responders…

Deceased

Tenure < 3

Months

Do Not

Mail

No Longer

Customers

No

Discount

Campaign

Selection

Rules

Global Suppression

Rules

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Even when the application isn’t a predictive model…

• We can still use historical data to better understand our customers

• Find strong correlators or drivers of behaviour

• Carry out text mining and analyse changing sentiment

• Develop a segmentation strategy that is data-driven

• Go beyond recency, frequency monetary to incorporate

– Who – Demographics

– What – Product/Service Categories

– Interactions – Web, Call Centre, Payment methods

– History – Tenure, Customer Journey,

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Example: How are your customers characterised?

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Using data to uncover customer segments

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Deeper customer understanding = more appropriate interactions

Pioneers

Cautious Spenders

Loyal Latents

Ad-hoc Big-Buyers

High-Maintenance

Low-Yield

Silent

Steady

Endangered

Exotics

DND

Business Bedrock

Techniques like cluster

analysis can uncover

subtle differences and

previously hidden

groups

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By utilising a powerful, proven methodology

• CRISP-DM: Cross-Industry Standard

Process for Data Mining

• Each application can be developed

and progressed through a series of

key phases

• www.CRISP-DM.eu

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Transaction Data

Competi

tiv

e

adv

anta

ge

By exploiting a wide data landscape

Descriptive Data

Interaction Data

Social Media Data

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By using powerful IBM advanced analytics technology

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By integrating the resultant insight with existing systems

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What do we offer Anna?

• 31 years old

• Estimated income > £28K

• On average spends £26

• Usually pays with credit card

• Not eligible for discount offer

• In the last 6 weeks bought these items -

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AEGON

• Improved customer response by 78%

through a targeted direct marketing

campaign that precisely reflects customer

needs

• Increased policy purchases by 3% , from

5% to 8%

• Expected to boost cross-sell opportunities

by selecting qualified customers and

predicting type of insurance offerings

needed

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Eircom

• “Our analytics team discovered that customer

experiences during the on-boarding process

when they join our mobile network have a

significant effect on the likelihood of churn. ”

• Enabled eircom to identify the most effective

ways to improve the customer experience –

reducing churn on key customer journeys by

around 6%

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C Spire Wireless

• United States’ largest privately held wireless

communications company

• Enhanced customer satisfaction levels

• Improved the effectiveness of retention

campaigns by 50 %

• Boosts cross-selling and up-selling, with sales

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Common Misunderstandings

• Revolutionary results overnight!

• You’ll need a Ph.D.

– In fact , data–literate, business focussed people learn how to do this

all the time.

• The more accurate the model the better

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Advice to get started

• Build Internal Credibility: Think about where you would get biggest impact for the

least effort.

• Consider adopting a proven methodology e.g. CRISP-DM (

www.CRISP-DM.eu

)

• Don’t get hung up on modelling techniques - focus on Business Understanding and

Deployment

• Consider the full data landscape

• Consider the sorts of roles involved /impacted

• Consider integration with other business insight systems (e.g. MI/BI)

• How will you know its worked? Focus on measuring the benefit – e.g. response

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Working with Smart Vision Europe Ltd

• As a premier partner we sell the IBM SPSS suite of software to you directly

– We’re agile, responsive and generally easier to deal with

• As experts in SPSS / Analytics / Predictive Analytics we will

– deliver classroom training courses

– offer side by side training support

– offer “skills transfer” consulting

– run booster and refresher sessions to get more from your SPSS licences

– Give no strings attached advice

• We are a support providing partner so if you already have SPSS you can source

your technical support directly from us (identical costs to IBM)

– We offer telephone support with real people as well as web tickets / email queries

– We offer “how to” support to help you get moving on your project quickly

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Thank you

Contact us:

+44 (0)207 786 3568

[email protected]

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